Artificial Intelligence, Machine Learning, and Deep Learning...Artificial Intelligence, Machine Learning, and Deep Learning Laurent Charlin HEC Montréal Mila, Quebec Artificial

Artificial Intelligence, Machine Learning, and

Deep Learning

Laurent Charlin HEC Montréal

Mila, Quebec Artificial Intelligence Institute

Progressive Growing of GANs for Improved Quality, Stability, and Variation Karras et al., ICLR’18

Progressive Growing of GANs for Improved Quality, Stability, and Variation Karras et al., ICLR’18

All computer generated

Deep Visual-Semantic Alignments for Generating Image Descriptions A. Karpathy, L. Fei-Fei, CVPR’15



Automatic Translation

https://ai.googleblog.com/2016/11/zero-shot-translation-with-googles.html

Automatic Translation

https://ai.googleblog.com/2016/11/zero-shot-translation-with-googles.html

Mnih et al. Nature Volume 518, pages 529–533 (26 February 2015)

AI for video games

https://www.youtube.com/watch?v=V1eYniJ0Rnk&vl=en

Esteva et al. Nature volume 542, pages 115–118 (02 February 2017)

AI for automatic skin cancer detection

9

Academia

Corporations

Governments

Introduction to Artificial intelligence

1. The technology

• Machine learning, deep learning, neural networks

2. Why now?

• Data, hardware, software

10

Artificial intelligence Make intelligent machines

Idea: Hugo Larochelle


Machine learning Make machines that can learn




Deep learning A set of machine learning techniques

based on neural networks


12


• Why is learning useful?

12



• Program computers to be intelligent

12




• Imagine you want a computer to recognize digits:

• Describe what a “1” should look like


• …

12







• …

12


• High variability






• …

12








• …

12








• …

12








• …

12



• Time consuming

• Lack of robustness






• …

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• Time consuming

• Lack of robustness

• Think of recognizing more complicated object (e.g., animals)

13


• How do children learn?

• Using “examples”

13


• How do children learn?

• Using “examples”

• Machine learning

• Present examples and labels to the computer

13


two

Example Label

sevenfour

two

Example Label

seven

two

Example Label

two

Example Label

Training: Process of learning using examples

two

Example Label


two

Example Label


?

Testing: Evaluate the performance of the computer

(Statistical) model of the data

Linear regression model

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Non-Linear regression model

y = f(β0 + β1x1 + β2x2 + . . .) + ϵ<latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit><latexit sha1_base64="(null)">(null)</latexit>

Non-Linear regression model

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Neural Network model (a type of non-linear regression)

Biologically inspired models

Neural network model of the data Deep

Caption generation

The cat eats grass Le chat mange de l’herbe

Translation

The cat eats grass

Image generation

The cat eats grass

Speech recognition

Recommendation

Sales

How good is my advertisement?

Will my team make the playoffs?

…

Financial predictions

Buy / Sell / Hold

Financial predictions

Buy / Sell / Hold

Should bail be given?

…

Machine Learning and Statistics (in 1 slide)

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• Machine learning is most often about predictions

• Performance on out-of-sample data

• Hypothesis testing & confidence intervals rarely considered

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• Machine learning is most often about predictions

• Performance on out-of-sample data

• Hypothesis testing & confidence intervals rarely considered

• Bayesian machine learning approaches sometimes provide the best of both worlds

29

Why now?

• Availability of large datasets

31

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

Idea: Alain Tapp

• The number of examples required relates to the complexity of the task

• Recognizing digits 10K examples

• Recognizing objects in images 10M examples

• Chatbots ?

38

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four

• “Marketplace for work that requires human intelligence”. (http://mturk.com)

39

Example Label

http://mturk.com

four


• Requesters propose tasks (HITs) to workers

39

Example Label

http://mturk.com

four



• Each task is worth a certain amount of money

39

Example Label

http://mturk.com

four



• Each task is worth a certain amount of money

• Mechanisms to ensure the quality of the results

39

Example Label

http://mturk.com

Hardware resources

40

Hardware resources

• Before 2010: Use a faster computer

40

Hardware resources


• Now: specialized hardware

40

Hardware resources



• Graphical processing units (GPU)

• Specialized hardware for linear algebra operations

40

Hardware resources



• Graphical processing units (GPU)

• Specialized hardware for linear algebra operations

• Tensorflow processing unit (TPU) or other even more-tailored hardware

40

https://cloud.google.com/blog/big-data/2017/05/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu

Performance in terms of number of predictions per seconds

Fast Computer

GPU

TPU




Performance in terms of number of predictions per seconds

Fast Computer

GPU

TPU



Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks

Theano, Torch, Tensorflow

Libraries

Programing Languages

Computer

Models

Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks


Libraries


Computer

Models

Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks


Libraries


Computer

Models

Software

42

python, lua, C++

CPU, GPU, clusters

Neural Networks


Libraries


Computer

Models

• These libraries have had a transformative effect

• We can explore models much faster than we before

43

Knowledge dissemination

• Data:

• Publishing dataset is becoming the new norm

• Most important datasets are publicly available

• Software:

• Libraries are open source

• Researchers are encouraged to share their code

• Ideas: interesting results are shared with the community in a matter of months (e.g., using arXiv.org)

44

http://arXiv.org

Takeaways

45

Takeaways• Machine learning is a subfield of artificial intelligence

45


• Neural networks are machine learning models

• They learn from examples

45


• Neural networks are machine learning models

• They learn from examples

• Essential ingredients for neural nets

1. Large amounts of data

2. Specialized hardware

3. Software Stack

• Current neural nets are close to human performance in some domains

45

Learning Visual Reasoning Without Strong Priors, Perez et al., 2017


How many yellow things are there?


How many yellow things are there? 2



2How many cyan things are there?

2




2

Are there as many yellow things as cyan things?




2

Are there as many yellow things as cyan things? No

Some Challenges for deep learning

47


47


1. How do we learn higher-level reasoning?

47



2. How much data do we need to solve more complicated tasks? E.g., chatbots

47




• Will we ever collect enough data?

47





3. How de we build systems we can trust (ethical, minimize bias)?

47





3. How de we build systems we can trust (ethical, minimize bias)?

4. …

47

How will AI impact the world?

48


• Science fiction scenarios are unlikely

48



• “Yes they [neural nets] can do great things, yes we can build companies around them, and yes they’ll change the economy but we are not there yet”

— Micheal I. Jordan (UC Berkeley)

48



• “Yes they [neural nets] can do great things, yes we can build companies around them, and yes they’ll change the economy but we are not there yet”

— Micheal I. Jordan (UC Berkeley)

• Major economic disruptions?

48

Thanks!

Documents

Artificial Intelligence, Machine Learning, and Deep Learning...Artificial Intelligence, Machine Learning, and Deep Learning Laurent Charlin HEC Montréal Mila, Quebec Artificial